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Tag selected: workload.
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Saved by uncleflo on July 24th, 2019.
Many customers want a disaster recovery environment, and they want to use this environment daily and know that it's in sync with and can support a production workload. This leads them to an active-active architecture. In other cases, users like Netflix and Lyft are distributed over large geographies. In these cases, multi-region active-active deployments are not optional. Designing these architectures is more complicated than it appears, as data being generated at one end needs to be synced with data at the other end. There are also consistency issues to consider. One needs to make trade-off decisions on cost, performance, and consistency. Further complicating matters is the variety of data stores used in the architecture results in a variety replication methods. In this session, we explore how to design an active-active multi-region architecture using AWS services, including Amazon Route 53, Amazon RDS multi-region replication, AWS DMS, and Amazon DynamoDB Streams. We discuss the challenges, trade-offs, and solutions.
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Saved by uncleflo on July 11th, 2019.
It’s a few weeks after AWS re:Invent 2018 and my head is still spinning from all of the information released at this year’s conference. This year I was able to enjoy a few sessions focused on Aurora deep dives. In fact, I walked away from the conference realizing that my own understanding of High Availability (HA), Disaster Recovery (DR), and Durability in Aurora had been off for quite a while. Consequently, I decided to put this blog out there, both to collect the ideas in one place for myself, and to share them in general. Unlike some of our previous blogs, I’m not focused on analyzing Aurora performance or examining the architecture behind Aurora. Instead, I want to focus on how HA, DR, and Durability are defined and implemented within the Aurora ecosystem. We’ll get just deep enough into the weeds to be able to examine these capabilities alone.
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Saved by uncleflo on May 12th, 2019.
This guide looks at the importance of containers in cloud computing, highlighting the benefits and showing how containers figure into such technologies as Docker, Kubernetes, Istio, VMs, and Knative. A container is a small file that packages together application code along with all the libraries and other dependencies that it needs to run. By packaging together applications, libraries, environment variables, other software binaries and configuration files, a container guarantees that it has everything needed to run the application out of the box, regardless of the operating environment in which the container runs. A key characteristic of a container is that it is small and fast because it uses some of the underlying host operating system's resources to run rather than containing a whole OS of its own.
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